H.3.2.6. Games and infotainment
Mohammadreza Mohammadnejad; Morteza Dorrigiv; Farzin Yaghmaee
Abstract
Research in recommender systems has largely relied on standardized datasets such as MovieLens, Amazon Reviews, and Last.fm. However, these datasets are unsuitable for in-game recommendations, particularly in Multiplayer Online Battle Arenas (MOBAs), due to the sequential, team-based, and adversarial ...
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Research in recommender systems has largely relied on standardized datasets such as MovieLens, Amazon Reviews, and Last.fm. However, these datasets are unsuitable for in-game recommendations, particularly in Multiplayer Online Battle Arenas (MOBAs), due to the sequential, team-based, and adversarial nature of gameplay. To identify essential characteristics for in-game recommendation datasets, we perform a cross-domain analysis of widely used recommendation datasets, evaluating their structural and distributional properties, including interaction space, matrix shape, sparsity, and Gini-based feature–shape diversity. Building on these insights, we curate DOTA-Draft, a research-ready dataset from raw professional Dota 2 matches, encoding sequential pick/ban states, patch versions, and match outcomes. Using this dataset, we conduct top-k drafting recommendation tasks and provide baseline results with Bayesian Personalized Ranking (BPR) and GRU4Rec. To facilitate adoption, DOTA-Draft is packaged in a RecBole-compatible format. This work establishes principled benchmarks for in-game recommendation, demonstrates the inadequacy of traditional user–item paradigms in dynamic, adversarial environments, and provides a foundation for developing models that account for sequential, multi-agent decision-making.
H.3.2.6. Games and infotainment
Shaqayeq Saffari; Morteza Dorrigiv; Farzin Yaghmaee
Abstract
Procedural Content Generation (PCG) through automated and algorithmic content generation is an active research field in the gaming industry. Recently, Machine Learning (ML) approaches have played a pivotal role in advancing this area. While recent studies have primarily focused on examining one or a ...
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Procedural Content Generation (PCG) through automated and algorithmic content generation is an active research field in the gaming industry. Recently, Machine Learning (ML) approaches have played a pivotal role in advancing this area. While recent studies have primarily focused on examining one or a few specific approaches in PCG, this paper provides a more comprehensive perspective by exploring a wider range of approaches, their applications, advantages, and disadvantages. Furthermore, the current challenges and potential future trends in this field are discussed. Although this paper does not aim to provide an exhaustive review of all existing research due to the rapid and expansive growth of this domain, it is based on the analysis of selected articles published between 2020 and 2024.
